DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgement is made of Applicant’s claim of priority as a Continuation of PCT/CN2020/079461, filed March 16, 2020.
Status of Claims
Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed January 6, 2026 have been fully considered but they are not persuasive. Applicant argues that the Zheng reference does not teach the limitation of “determining a type of the target video, wherein the type of the target video is associated with content of the target video”. Examiner respectfully disagrees. As described in the 35 USC 103 rejections below, Zheng teaches determining a keyword representing the category of the target video clip (i.e., a type of the target video). The keyword determining method is to determine the recognition result for the image frame included in the target video clip as the keyword of the target video clip. The program recognizes information (e.g., the number of objects and a movement of a person included in each image frame) as the keyword of the target video clip (see Zheng, Paras. [0061] and [0065]). Applicant argues that Zheng’s keyword is merely an “image label” and does not correspond to the type of the target video. However, Applicant is reminded that the Specification of the invention is not read into the claims. Based on the broadest reasonable interpretation of “determining a type of the target video, wherein the type of the target video is associated with the content of the target video”, Zheng’s teaching of determining a keyword that represents the category of the video clip is sufficient to teach this limitation because the keyword representing the category is describing the type of the video based on the content in the video. Therefore, Zheng is sufficient to teach this limitation.
Applicant further argues that the Bai reference does not teach “establishing a target editing model for the target video according to the type of the target video”. Examiner respectfully disagrees. As described in the 35 USC 103 rejections below, Bai teaches a plurality of video editing models associated with one or more types of the operation data (see Bai, Para. [0057]). For example, if a user input is a POI, the processing unit may identify a video editing model corresponding to the POI, search from operation data to identify operation data corresponding to the POI, identify video data corresponding to POI operation data, and generate a video editing interface to present the identified video data (see Bai, Para. [0097]). Applicant argues that the operation data of Bai merely includes information associated with the movable object rather than the tracked object, let alone a type of video data or a type of the tracked object. Applicant further argues that the POI of Bai refers to a user-defined point of interest or a flight mode and cannot constitute teaching a type of video. However, Examiner asserts that regardless of if the POI is a user-input, based on the broadest reasonable interpretation of the limitation, the POI can still describe the type of the target video, and a target editing model can be selected corresponding to the POI. Additionally, even if the operation data is associated with the object recording the video rather than the video itself, the target editing model is still established based upon the content of the video (i.e., the POI recorded in the video) and therefore is sufficient in teaching the limitation. Therefore, the 35 USC 103 rejection of the claims is upheld, and consequently, THIS ACTION IS FINAL.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-7, 10-11, 13, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2016/03773817 A1) in view of Zheng (US 2019/0377956 A1) further in view of Bai (US 2019/0164575 A1).
Regarding claim 1, Drake teaches a method for generating a synopsis video, comprising:
acquiring a target video and parameter data related to editing of the target video (Drake, Para. [0022], generating a short-form version of media content from traditional long-form media content, such as a movie, documentary or other video (i.e., target video). A desired temporal parameter (i.e., parameter data related to editing of the target video) associated with a media content is received from a user),
wherein the parameter data comprises at least a duration parameter of a synopsis video of the target video (Drake, Para. [0022], a user/consumer of media content may specify a time limit or duration within which the consumer wishes to have a short-form version of the media content);
extracting a plurality of pieces of image data from the target video (Drake, Para. [0028], generate metadata describing objects an actions based on the temporal nature of the movie at a frame level, camera shot level, cut level, or scene level), and
determining an image label of each piece of the plurality of pieces of the image data, wherein the image label comprises at least a visual-type label (Drake, Para. [0028], generate metadata describing objects and actions based on the temporal nature of the movie at a frame level, camera shot level, cut level, or scene level. Examples of metadata include, but are not limited to, information identifying locations, characters, actions, music, objects associated with a scene, a short scene, or a cut or individual frame); and
editing the target video according to the image label of the image data in the target video (Drake, Para. [0028], given some long-form version of media content, temporal and/or story-based metadata can be generated and used to tag corresponding portions or sections of the move, allowing key scenes or portions of scenes that contribute to a given story arc can be identified. Para. [0029], a temporal engine analyzing metadata-tagged media content and outputting a dynamically generated short-form version (i.e., synopsis video) of the media content).
Although Drake teaches generating metadata for each frame of the target video (Drake, Para. [0028), Drake does not explicitly teach “determining a type of the target video, wherein the type of the target video is associated with content of the target video”. However, in an analogous field of endeavor, Zheng teaches determining a keyword representing the category of the target video clip (i.e., a type of the target video). The keyword determining method is to determine the recognition result for the image frame included in the target video clip as the keyword of the target video clip (Zheng, Para. [0065]). The program recognizes information (e.g., the number of objects and a movement of a person included in each image frame) as the keyword of the target video clip (Zheng, Para. [0061]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake with the teachings of Zheng by including determining a category or type of the target video associated with the content of the target video. One having ordinary skill in the art would have been motivated to combine these references, because doing so would allow for categorizing a video, as recognized by Zheng.
Although Drake in view of Zheng teaches generating a short-form version of media content according to the desired temporal parameter (i.e., duration parameter) and preserving a predetermined thematic aspect of the media content (i.e., type of video) (Drake, Para. [0022]), they do not explicitly teach “establishing a target editing model for the target video according to the type of the target video, the duration parameter, and a plurality of preset editing technique submodels”. However, in an analogous field of endeavor, Bai teaches a plurality of video editing models associated with one or more types of the operation data (Bai, Para. [0057]). For example, if a user input is a POI, the processing unit may identify a video editing model corresponding to the POI, search from operation data to identify operation data corresponding to the POI, identify video data corresponding to POI operation data, and generate a video editing interface to present the identified video data (Bai, Para. [0097]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake in view of Zheng with the teachings of Bai by including establishing and using a target editing model based on a plurality of video editing models that are associated with operation data, such as the type of video and duration parameter as taught by Drake and Zheng. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for efficient video editing, as recognized by Bai. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 5, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, and further teaches wherein the preset editing technique submodels comprise at least one of:
an editing technique submodel corresponding to a camera shot editing technique, an editing technique submodel corresponding to an indoor/outdoor scene editing technique, an editing technique submodel corresponding to an emotional fluctuation editing technique, an editing technique submodel corresponding to a dynamic editing technique, an editing technique submodel corresponding to a recency effect editing technique, an editing technique submodel corresponding to a primacy effect editing technique, or an editing technique submodel corresponding to a suffix effect editing technique (Bai, Para. [0099], the video editing models may include a model for remixing the identified video data (i.e., dynamic editing technique).
The proposed combination as well as the motivation for combining the Drake, Zheng, and Bai references presented in the rejection of Claim 1, apply to Claim 5 and are incorporated herein by reference. Thus, the method recited in Claim 5 is met by Drake in view of Zheng further in view of Bai.
Regarding claim 6, Drake in view of Zheng further in view of Bai teaches the method according to claim 5, and further teaches wherein the preset editing technique submodels are generated in following manner:
determining a plurality of editing rules corresponding to a plurality of editing technique types according to editing characteristics of editing techniques of different types; and establishing a plurality of preset editing technique submodels corresponding to the plurality of editing technique types according to the plurality of editing rules (Bai, Para. [0098], the video editing model my include identification rules to identify POI operation data and association rules between the operation data and the video data. Accordingly, the processing unit may identify operation data corresponding to the mode and video data corresponding to the identified operation data, based on the identification and association rules.).
The proposed combination as well as the motivation for combining the Drake, Zheng, and Bai references presented in the rejection of Claim 1, apply to Claim 6 and are incorporated herein by reference. Thus, the method recited in Claim 6 is met by Drake in view of Zheng further in view of Bai.
Regarding claim 7, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, and further teaches wherein the visual-type label comprises at least one of: a text label, an article label, a face label, an aesthetic factor label, or an emotional factor label (Drake, Para. [0028], generate metadata describing objects and actions based on the temporal nature of the movie at a frame level. Examples of metadata include, but are not limited to, information identifying locations, characters, actions, music, or objects associated with a scene).
Regarding claim 10, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, and further teaches wherein the image label further comprises a structure-type label (Drake, Para. [0028], generate metadata describing objects and actions based on the temporal nature of the movie at a frame level. Examples of metadata include, but are not limited to, information identifying locations, characters, actions, music, or objects associated with a scene).
Regarding claim 11, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, and further teaches wherein the image label further comprises a structure-type label (Drake, Para. [0028], generate metadata describing objects and actions based on the temporal nature of the movie at a frame level. Examples of metadata include, but are not limited to, information identifying locations, characters, actions, music, or objects associated with a scene).
Regarding claim 13, Drake in view of Zheng further in view of Bai teaches the method according to claim 11, and further teaches wherein in a case that the image label comprises the time domain attribute label, the determining the image label of image data comprises:
determining a time point of the image data in the target video (Drake, Para. [0028], examples of metadata include a cut or individual frame identified by a timecode);
determining a time domain corresponding to the image data according to the time point of the image data in the target video and a total duration of the target video, wherein the time domain comprises a head time domain, a tail time domain, and an intermediate time domain (Drake, Para. [0030], dynamically create a 15-minute version of the feature-length media content that includes a beginning, middle, and end); and
determining the time domain attribute label of the image data according to the time domain corresponding to the image data (Drake, Para. [0028], examples of metadata include a cut or individual frame identified by a timecode).
Regarding claim 17, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, and further teaches wherein the parameter data further comprises a type parameter used for indicating the type of the target video (Zheng, Para. [0065], the server acquires the target video and the target video element information. Based on the preset corresponding relationship between the video element information and the keyword determining method for the video clip, the server obtains the keyword representing the category of the target video clip).
The proposed combination as well as the motivation for combining the Drake, Zheng, and Bai references presented in the rejection of Claim 1, apply to Claim 17 and are incorporated herein by reference. Thus, the method recited in Claim 17 is met by Drake in view of Zheng further in view of Bai.
Claim 18 recites a system with elements corresponding to the steps recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Drake, Zheng, and Bai references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Drake, Zheng, and Bai references discloses a memory and a processor (Drake, Para. [0023], user device may include a processor and a memory unit).
Claim 20 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Drake, Zheng, and Bai references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Drake, Zheng, and Bai references discloses a computer readable storage medium (Drake, Para. [0005], a non-transitory computer readable medium has computer executable program code embodied thereon).
Claims 2, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2016/03773817 A1) in view of Zheng (US 2019/0377956 A1) further in view of Bai (US 2019/0164575 A1), as applied to claims 1, 5-7, 10-11, 13, 17-18, and 20 above, and further in view of Gargi (US 2005/0207733 A1).
Regarding claim 2, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, as described above.
Although Drake in view of Zheng further in view of Bai teaches establishing and using a target editing model based on a plurality of video editing models that are associated with operation data (Bai, Para. [0057]), they do not explicitly teach “wherein the establishing a target editing model for the target video according to the type of the target video, the duration parameter, and the plurality of preset editing technique submodels comprises: determining, from weight parameter groups of a plurality of groups of preset editing technique submodels according to the type of the target video, a weight parameter group of a preset editing technique submodel matching the type of the target video as a target weight parameter group, where the target weight parameter group comprises preset weights that respectively correspond to the plurality of preset editing technique submodels” and “establishing the target editing model for the target video according to the target weight parameter group, the duration parameter, and the plurality of preset editing technique submodels”. However, in an analogous field of endeavor, Gargi teaches assigning weights to each type of video analysis technique, for example, if speech is considered more important than other video features (e.g., laughter, motion, etc.) for a digital video (i.e., the type of video), a larger multiplier may be used to augment the weight of the score assigned to each frame of the digital video when speech is detected (Gargi, Para. [0028]). The weight given to each type of video analysis technique may be assigned prior to performing an analysis of a digital video (i.e., preset) or at the end of an analysis (Gargi, Para. [0029]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake in view of Zheng further in view of Bai by including a target weight parameter group that includes preset weights assigned to each preset editing technique submodel based on the type of video and determining the target editing model based on the target weight parameter group. One having ordinary skill in the art would have been motivated to combine these references, because doing so would provide an improved browsing experience for digital videos, as recognized by Gargi. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 16, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, as described above.
Although Drake in view of Zheng further in view of Bai teaches establishing and using a target editing model based on a plurality of video editing models that are associated with operation data (Bai, Para. [0057]), they do not explicitly teach ‘wherein the parameter data further comprises a customized weight parameter group”. However, in an analogous field of endeavor, Gargi teaches assigning weights to each type of video analysis technique, for example, if speech is considered more important than other video features (e.g., laughter, motion, etc.) for a digital video (i.e., the type of video), a larger multiplier may be used to augment the weight of the score assigned to each frame of the digital video when speech is detected (Gargi, Para. [0028]). The weight given to each type of video analysis technique may be assigned prior to performing an analysis of a digital video (i.e., preset) or at the end of an analysis (Gargi, Para. [0029]).
The proposed combination as well as the motivation for combining the Drake, Zheng, Bai, and Gargi references presented in the rejection of Claim 2, apply to Claim 16 and are incorporated herein by reference. Thus, the method recited in Claim 16 is met by Drake in view of Zheng further in view of Bai and Gargi.
Claim 19 recites a system with elements corresponding to the steps recited in Claim 2. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Drake, Zheng, Bai and Gargi references, presented in rejection of Claim 2, apply to this claim. Finally, the combination of the Drake, Zheng, Bai, and Gargi references discloses a memory and a processor (Drake, Para. [0023], user device may include a processor and a memory unit).
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2016/03773817 A1) in view of Zheng (US 2019/0377956 A1) further in view of Bai (US 2019/0164575 A1) and Gargi (US 2005/0207733 A1), as applied to claims 2, 16, and 19 above, and further in view of Zhang (US 2021/0133457 A1, with foreign priority to CN 201811437221, filed November 28, 2018, US PGPub used herein as a translation).
Regarding claim 3, Drake in view of Zheng further in view of Bai and Gargi teaches the method according to claim 2, as described above.
Although Drake in view of Zheng further in view of Bai and Gargi teaches preset weights to different video analysis techniques (i.e., editing technique submodels) based on the video type (Gargi, Para. [0028]), they do not explicitly teach “where the weight parameter groups of the plurality of groups of preset editing technique submodels are acquired in following manner: acquiring a sample video and a sample synopsis video of the sample video as sample data, wherein the sample video comprises videos of a plurality of types”, “labeling the sample data to obtain labeled sample data” and “learning the labeled sample data, and determining the weight parameter groups of the plurality of groups of preset editing technique submodels corresponding to the videos of the plurality of types”. However, in an analogous field of endeavor, Zhang teaches training a video action classification model based on training samples, where the training samples include multiple groups of video frames and the standard classification category information corresponding to respective on of the multiple groups (Zhang, Para. [0030]). The three-dimensional convolution neural network module can extract the spatial feature information corresponding to respective one of groups of video frames in response to that inputting multiple groups in the training samples into the three-dimensional convolution neural network module. The feature fusion may be performed on the spatial feature information and optical flow information corresponding to each group, and the second classifier module can output the classification category prediction information corresponding to each group of video frames (Zhang, Para. [0036]). The difference between the classification category prediction information and the standard classification category information corresponding to each group of video frames is determined. Then the weight parameters in the video action classification model may be adjusted based on the difference information corresponding to each group of video frames (Zhang, Para. [0037]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake in view of Zheng further in view of Bai and Gargi with the teachings of Zhang by including acquiring multiple groups of training sample videos (i.e., a plurality of types) with standard classification category information (i.e., labeled sample data) and determining the weight parameters using the labeled sample data. One having ordinary skill in the art would have been motivated to combine these references, because doing so would allow for determining the actions of objects in a short video, as recognized by Zhang. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 4, Drake in view of Zheng further in view of Bai, Gargi, and Zhang teaches the method according to claim 3, and further teaches wherein the labeling the sample data comprises:
labeling a type of the sample video in the sample data (Zhang, Para. [0030], the training samples include multiple groups of video frames and the standard classification category information corresponding to respective on of the multiple groups); and
determining and labeling, according to the sample video and the sample synopsis video in the sample data, an image label of image data comprised in the sample synopsis video from the sample data (Drake, Para. [0028], generate metadata describing objects and actions based on the temporal nature of the movie at a frame level, camera shot level, cut level, or scene level. Examples of metadata include, but are not limited to, information identifying locations, characters, actions, music, objects associated with a scene, a short scene, or a cut or individual frame) and an editing technique type corresponding to the sample synopsis video (Bai, Para. [0097], , if a user input is a POI, the processing unit may identify a video editing model corresponding to the POI, search from operation data to identify operation data corresponding to the POI, identify video data corresponding to POI operation data, and generate a video editing interface to present the identified video data).
The proposed combination as well as the motivation for combining the Drake, Zheng, Bai, Gargi, and Zhang references presented in the rejection of Claim 3, apply to Claim 4 and are incorporated herein by reference. Thus, the system recited in Claim 4 is met by Drake in view of Zheng further in view of Bai, Gargi, and Zhang.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2016/03773817 A1) in view of Zheng (US 2019/0377956 A1) further in view of Bai (US 2019/0164575 A1), as applied to claims 1, 5-7, 10-11, 13, 17-18, and 20 above, and further in view of Shetty (US 2016/0070962 A1).
Regarding claim 8, Drake in view of Zheng further in view of Bai teaches the method according to claim 7, as described above.
Although Drake in view of Zheng further in view of Bai teaches determining label for each frame in a target video (Drake, Para. [0028]), they do not explicitly teach “wherein in a case that the image label comprises the aesthetic factor label, the determining the image label of image data comprises: invoking a preset aesthetic scoring model to process the image data to obtain a corresponding aesthetic score, wherein the aesthetic score is used for representing attractiveness generated to a user from the image data based on picture aesthetic” and “determining the aesthetic factor label of the image data according to the aesthetic score”. However, in an analogous field of endeavor, Shetty teaches aesthetic scores to assist in selection of a representative frame that is also aesthetically please. The aesthetic score is determined for each frame using individual qualities such as the amount of motion, sharpness, distance from segment boundary, and photo quality (Shetty, Para. [0044]). Shetty further teaches a semantic concept is a label assigned to the content of a video or frame (Shetty, Para. [0036]), and that after determination of the combined score for each frame in the segment, the frame selection module selects the highest-ranked frame as the representative frame and the frame is stored with its semantic concept (i.e., determined aesthetic factor label) (Shetty, Para. [0048]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake in view of Zheng further in view of Bai with the teachings of Shetty by including obtaining an aesthetic score of the image data and determining the associated label with the aesthetic frame. One having ordinary skill in the art would have been motivated to combine these references, because doing so would allow for selecting representative video summaries using semantic features, as recognized by Shetty. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2016/03773817 A1) in view of Zheng (US 2019/0377956 A1) further in view of Bai (US 2019/0164575 A1), as applied to claims 1, 5-7, 10-11, 13, 17-18, and 20 above, and further in view of Barari (US 2018/0349688 A1).
Regarding claim 9, Drake in view of Zheng further in view of Bai teaches the method according to claim 7, as described above.
Although Drake in view of Zheng further in view of Bai teaches determining labels for each frame in a target video (Drake, Para. [0028]), they do not explicitly teach “wherein in a case that the image label comprises the emotional factor label, the determining the image label of image data comprises: invoking a preset emotional scoring model to process the image data to obtain a corresponding emotional score, wherein the emotional score is used for representing attractiveness generated to a user from the image data based on emotional interest” and “determining the emotional factor label of the image data according to the emotional score”. However, in an analogous field of endeavor, Barari teaches a score determination module may determine a score for each of the plurality of behavioural parameters obtained for the extracted frames. For example, the score for emotion behavioural parameters may be “anger”=0.4, “happiness”=0.05, “sadness”=0.3, “depression”=0.25 (Barari, Para. [0044]). Barari further teaches determining the intent of the subject based on the emotion analysed for the subject. For example, if the negative weighted average score is increasing over the pre-defined frames, then the intent of the subject is determined as “deceitful” (Barari, Para. [0048]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake in view of Zheng further in view of Bai with the teachings of Barari by including determining an emotional score for the frames of the video and determining the emotional factor label (i.e., intent) based on the emotion score. One having ordinary skill in the art would have been motivated to combine these references, because doing so would allow for behavioural analysis from a captured video, as recognized by Barari. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2016/03773817 A1) in view of Zheng (US 2019/0377956 A1) further in view of Bai (US 2019/0164575 A1), as applied to claims 1, 5-7, 10-11, 13, 17-18, and 20 above, and further in view of Bateman (US 2006/0103732 A1).
Regarding claim 12, Drake in view of Zheng further in view of Bai teaches the method according to claim 11, and further teaches wherein in a case that the image label comprises the dynamic attribute label, the determining the image label of image data comprises:
determining the dynamic attribute label of the image data according to the action of the target object (Drake, Para. [0028], generate metadata (i.e., labels) describing objects and actions based on the temporal nature of the movie at a frame level. Examples of metadata include actions).
Although Drake in view of Zheng further in view of Bai teaches determining labels for each frame in a target video based on actions in the video (Drake, Para. [0028]), they do not explicitly teach “acquiring image data adjacent before and after the image data as reference data”, “acquiring a pixel indicating a target object in the image data as an object pixel, and acquiring a pixel indicating the target object in the reference data as a reference pixel”, “comparing the object pixel with the reference pixel to determine an action of the target object”. However, in an analogous field of endeavor, Bateman teaches applying a motion detection test by comparing each frame against the previous frame (i.e., image data and reference data). Differences between the two are flagged as motion. This could be done by comparing every pixel of one frame with every pixel of the next frame (Bateman, Para. [0038]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake in view of Zheng further in view of Bai with the teachings of Bateman by including determining the action of the target object by comparing the pixels in frames of a reference image and the image to detect motion. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for identifying motion within the target video, as recognized by Bateman. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2016/03773817 A1) in view of Zheng (US 2019/0377956 A1) further in view of Bai (US 2019/0164575 A1), as applied to claims 1, 5-7, 10-11, 13, 17-18, and 20 above, and further in view of Pham (US 10657176 B1).
Regarding claim 14, Drake in view of Zheng further in view of Bai teaches the method according to claim 1, as described above.
Although Drake in view of Zheng further in view of Bai teaches generating a short-form version of media content from traditional long-form median content, such as a movie, documentary, or other video (Drake, Para. [0022]), they do not explicitly teach “wherein the target video comprises a video for a commodity promotion scenario”. However, in an analogous field of endeavor, Pham teaches a video tagging system wherein the video is a promotional video advertising different items for decorating a house (Pham, Col. 1, lines 49-56).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Drake in view of Zheng further in view of Bai with the teachings of Pham by including the target video comprising a promotional video. One having ordinary skill in the art would have been motivated to combine these references, because doing so would provide a commodity promotion video from which to create a synopsis video. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 15, Drake in view of Zheng further in view of Bai and Pham teaches the method according to claim 14, and further teaches wherein the type of the target video comprises at least one of: a clothing type, a food type, or a cosmetics type (Pham, Col. 1 line 64-Col. 2 line 11, Based on knowledge of the product ID(s) that appears in the video, the video tagging system can use transcription data of audio content associated with the video to identify terms (or keywords) mentioned in the video that are relevant to one or more attributes of the object(s). Assuming the object is a product for sale, the attributes can include, e.g., a type of the product for sale (e.g., basketball, shoe, clothing item, etc.).
The proposed combination as well as the motivation for combining the Drake, Zheng, Bai, and Pham references presented in the rejection of Claim 14, apply to Claim 15 and are incorporated herein by reference. Thus, the method recited in Claim 15 is met by Drake in view of Zheng further in view of Bai and Pham.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emma Rose Goebel whose telephone number is (703)756-5582. The examiner can normally be reached Monday - Friday 7:30-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached at (571) 272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Emma Rose Goebel/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662